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Update for hdf5 structure reference
- added missing corr to inequiv information - added a Vasp specific section and added missing information
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Alexander Hampel
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378f743b09
@ -6,7 +6,7 @@ hdf5 structure
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All the data is stored using the hdf5 standard, as described also in the
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documentation of the TRIQS package itself. In order to do a DMFT calculation,
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using input from DFT applications, a converter is needed on order to provide
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the necessary data in the hdf5 format.
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the necessary data in the hdf5 format.
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groups and their formats
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------------------------
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@ -17,7 +17,7 @@ DMFT calculations for all kinds of situations, e.g. d-p Hamiltonians, more than
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one correlated atomic shell, or using symmetry operations for the k-summation.
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We store all data in subgroups of the hdf5 archive:
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Main data
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Main data
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^^^^^^^^^
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There needs to be one subgroup for the main data of the
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calculation. The default name of this group is `dft_input`. Its contents are
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@ -31,56 +31,93 @@ k_dep_projection numpy.int
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0 otherwise.
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SP numpy.int 1 for spin-polarised Hamiltonian, 0 for paramagnetic Hamiltonian.
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SO numpy.int 1 if spin-orbit interaction is included, 0 otherwise.
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charge_below numpy.float Number of electrons in the crystal below the correlated orbitals.
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Note that this is for compatibility with dmftproj.
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charge_below numpy.float Number of electrons in the crystal below the correlated orbitals.
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Note that this is for compatibility with dmftproj, otherwise set to 0
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density_required numpy.float Required total electron density. Needed to determine the chemical potential.
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The density in the projection window is then `density_required`-`charge_below`.
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symm_op numpy.int 1 if symmetry operations are used for the BZ sums,
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symm_op numpy.int 1 if symmetry operations are used for the BZ sums,
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0 if all k-points are directly included in the input.
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n_shells numpy.int Number of atomic shells for which post-processing is possible.
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Note: this is `not` the number of correlated orbitals!
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n_shells numpy.int Number of atomic shells for which post-processing is possible.
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Note: this is `not` the number of correlated orbitals!
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If there are two equivalent atoms in the unit cell, `n_shells` is 2.
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shells list of dict {string:int}, dim n_shells x 4 Atomic shell information.
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For each shell, have a dict with keys ['atom', 'sort', 'l', 'dim'].
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shells list of dict {string:int}, dim n_shells x 4 Atomic shell information.
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For each shell, have a dict with keys ['atom', 'sort', 'l', 'dim'].
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'atom' is the atom index, 'sort' defines the equivalency of the atoms,
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'l' is the angular quantum number, 'dim' is the dimension of the atomic shell.
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e.g. for two equivalent atoms in the unit cell, `atom` runs from 0 to 1,
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e.g. for two equivalent atoms in the unit cell, `atom` runs from 0 to 1,
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but `sort` can take only one value 0.
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n_corr_shells numpy.int Number of correlated atomic shells.
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If there are two correlated equivalent atoms in the unit cell, `n_corr_shells` is 2.
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corr_shells list of dict {string:int}, dim n_corr_shells x 6 Correlated orbital information.
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For each correlated shell, have a dict with keys
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['atom', 'sort', 'l', 'dim', 'SO', 'irep'].
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n_corr_shells numpy.int Number of correlated atomic shells.
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If there are two correlated equivalent atoms in the unit cell, `n_corr_shells` is 2.
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n_inequiv_shells numpy.int Number of inequivalent atomic shells. Needs to be smaller than `n_corr_shells`.
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The up / downfolding routines mediate between all correlated shells and the
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actual inequivalent shells, by using the self-energy etc. for all equal shells
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belonging to the same class of inequivalent shells. The mapping is performed with
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information stored in `corr_to_inequiv` and `inequiv_to_corr`.
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corr_to_inequiv list of numpy.int, dim `n_corr_shells` mapping from correlated shells to inequivalent correlated shells.
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A list of length `n_corr_shells` containing integers, where same numbers mark
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equivalent sites.
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inequiv_to_corr list of numpy.int, dim `n_inequiv_shells` A list of length `n_inequiv_shells` containing list indices as integers pointing
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to the corresponding sites in `corr_to_inequiv`.
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corr_shells list of dict {string:int}, dim n_corr_shells x 6 Correlated orbital information.
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For each correlated shell, have a dict with keys
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['atom', 'sort', 'l', 'dim', 'SO', 'irrep'].
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'atom' is the atom index, 'sort' defines the equivalency of the atoms,
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'l' is the angular quantum number, 'dim' is the dimension of the atomic shell.
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'SO' is one if spin-orbit is included, 0 otherwise, 'irep' is a dummy integer 0.
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use_rotations numpy.int 1 if local and global coordinate systems are used, 0 otherwise.
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rot_mat list of numpy.array.complex, Rotation matrices for correlated shells, if `use_rotations`.
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dim n_corr_shells x [corr_shells['dim'],corr_shells['dim']] Set to the unity matrix if no rotations are used.
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rot_mat list of numpy.array.complex, Rotation matrices for correlated shells, if `use_rotations`.
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dim n_corr_shells x [corr_shells['dim'],corr_shells['dim']] These rotations are automatically applied for up / downfolding.
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Set to the unity matrix if no rotations are used.
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rot_mat_time_inv list of numpy.int, dim n_corr_shells If `SP` is 1, 1 if the coordinate transformation contains inversion, 0 otherwise.
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If `use_rotations` or `SP` is 0, give a list of zeros.
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n_reps numpy.int Number of irreducible representations of the correlated shell.
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n_reps numpy.int Number of irreducible representations of the correlated shell.
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e.g. 2 if eg/t2g splitting is used.
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dim_reps list of numpy.int, dim n_reps Dimension of the representations.
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e.g. [2,3] for eg/t2g subsets.
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T list of numpy.array.complex, Transformation matrix from the spherical harmonics to impurity problem basis
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dim n_inequiv_corr_shell x normally the real cubic harmonics).
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[max(corr_shell['dim']),max(corr_shell['dim'])] This matrix is used to calculate the 4-index U matrix.
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e.g. [2,3] for eg/t2g subsets.
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T list of numpy.array.complex, Transformation matrix from the spherical harmonics to impurity problem basis
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dim n_inequiv_corr_shell x normally the real cubic harmonics).
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[max(corr_shell['dim']),max(corr_shell['dim'])] This matrix can be used to calculate the 4-index U matrix, not automatically done.
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n_orbitals numpy.array.int, dim [n_k,SP+1-SO] Number of Bloch bands included in the projection window for each k-point.
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If SP+1-SO=2, the number of included bands may depend on the spin projection up/down.
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proj_mat numpy.array.complex, Projection matrices from Bloch bands to Wannier orbitals.
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dim [n_k,SP+1-SO,n_corr_shells,max(corr_shell['dim']),max(n_orbitals)] For efficient storage reasons, all matrices must be of the same size
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(given by last two indices).
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dim [n_k,SP+1-SO,n_corr_shells,max(corr_shell['dim']),max(n_orbitals)] For efficient storage reasons, all matrices must be of the same size
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(given by last two indices).
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For k-points with fewer bands, only the first entries are used, the rest are zero.
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e.g. if number of Bloch bands ranges from 4-6, all matrices are of size 6.
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bz_weights numpy.array.float, dim n_k Weights of the k-points for the k summation.
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hopping numpy.array.complex, Non-interacting Hamiltonian matrix for each k point.
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dim [n_k,SP+1-SO,max(n_orbitals),max(n_orbitals)] As for `proj_mat`, all matrices have to be of the same size.
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bz_weights numpy.array.float, dim n_k Weights of the k-points for the k summation. Soon be replaced by `kpt_weights`
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hopping numpy.array.complex, Non-interacting Hamiltonian matrix for each k point.
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dim [n_k,SP+1-SO,max(n_orbitals),max(n_orbitals)] As for `proj_mat`, all matrices have to be of the same size.
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================= ====================================================================== =====================================================================================
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Converter specific data
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^^^^^^^^^^^^^^^^^^^^^^^
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This data is specific to the different converters and stored in the `dft_input`
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group as well.
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For the Vasp converter:
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================= ====================================================================== =====================================================================================
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Name Type Meaning
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================= ====================================================================== =====================================================================================
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kpt_basis numpy.array.float, dim 3x3 Basis for the k-point mesh, reciprocal lattice vectors.
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kpts numpy.array.float, dim n_k x 3 k-points given in reciprocal coordinates.
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kpt_weights numpy.array.float, dim n_k Weights of the k-points for the k summation.
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proj_or_hk string Switch determining whether the Vasp converter is running in projection mode `proj`, or
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in Hamiltonian mode `hk`. In Hamiltonian mode, the hopping matrix is written in
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orbital basis, whereas in projection mode hopping is written in band basis.
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proj_mat_csc numpy.array.complex, Projection matrices from Bloch bands to Wannier orbitals for Hamiltonian based `hk`
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dim approach. No site index is given, since hk is written in orbital basis. The last to
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[n_k,SP+1-SO, n_corr_shells x max(corr_shell['dim']), max(n_orbitals)] indices are a square matrix rotating from orbital to band space.
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dft_fermi_weights numpy.array.float, dim n_k x 1 x max(n_orbitals) DFT fermi weights (occupations) of KS eigenstates for each k-point for calculation
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(stored in dft_misc_input) of density matrix correction.
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band_window list of numpy.array.int , dim(SP+1-SO)x n_k x 2 Band windows as KS band indices in Vasp for each spin channel, and k-point. Needed for
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(stored in dft_misc_input) writing out the GAMMA file.
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Symmetry operations
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^^^^^^^^^^^^^^^^^^^
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^^^^^^^^^^^^^^^^^^^
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In this subgroup we store all the data for applying the symmetry operations in
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the DMFT loop (in case you want to use symmetry operations). The default name
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of this subgroup is `dft_symmcorr_input`. This information is needed only if symmetry
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@ -127,8 +164,8 @@ It is furthermore assumed that all k-points have equal weight in the k-sum.
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Note that the input file should contain only the numbers, not the comments
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given in above example.
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The Hamiltonian matrices can be taken, e.g., from Wannier90, which contructs
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the Hamiltonian in a maximally localised Wannier basis.
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The Hamiltonian matrices can be taken, e.g., from Wannier90, which constructs
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the Hamiltonian in a maximally localized Wannier basis.
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Note that with this simplified converter, no full charge self consistent
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calculations are possible!
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@ -109,7 +109,7 @@ class SumkDFT(object):
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# optional properties to load
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# soon bz_weights is depraced and replaced by kpt_weights, kpts_basis and kpts will become required to read soon
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optional_things_to_read = ['proj_mat_csc', 'proj_or_hk', 'kpts_basis','kpts','kpt_weights', 'kpt_basis']
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optional_things_to_read = ['proj_mat_csc', 'proj_or_hk', 'kpt_basis','kpts','kpt_weights']
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subgroup_present, self.optional_values_not_read = self.read_input_from_hdf(subgrp=self.dft_data, things_to_read=optional_things_to_read)
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if self.symm_op:
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